System for analyzing thermal data based on breast surface temperature to determine suspect conditions

ABSTRACT

A portable computing device or microprocessor/storage system including temperature sensors used to collect temperature readings of a breast tissue of a subject. The device would collect data from the sensors at regular time intervals over a period of time. All of the generated temperature data is stored in the portable computing or storage device. The sensors are placed on the greatest areas of interest on the breast, based on where most cancers develop, by using a sensor placeholder. The sensor placeholder would be lobate shaped, with the sensor placeholder aligning with the glandular regions of the breast where cancers are most likely to develop. The temperature data is then analyzed by one or more classifier systems and classified as either suspect or non-suspect tissue.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part and claims priority to U.S.patent application Ser. No. 12/198,967, filed Aug. 27, 2008 andincorporated herein by reference.

BRIEF DESCRIPTION OF THE INVENTION

A system for collecting temperature readings of breast tissue of asubject over a period of time based on a set of predetermined positionsaround the breast and analyzing those temperature readings through oneor more classifier systems to classify the breast tissue as eithersuspect or non-suspect tissue.

STATEMENTS AS TO THE RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSOREDRESEARCH OR DEVELOPMENT

Not applicable.

REFERENCE TO A “SEQUENCE LISTING,” A TABLE, OR A COMPUTER PROGRAMLISTING APPENDIX SUBMITTED ON A COMPACT DISK

Not applicable.

BACKGROUND OF THE INVENTION

Breast cancer continues to be the second leading cause of death forwomen between the ages of 40 to 55 in America. The number of womendeveloping breast cancer has increased tremendously from 1:20 in 1960 to1:7 today. Epidemiological studies estimate that one in eight women willdevelop breast cancer during their lifetimes. Moreover, one in fivewomen with breast cancer will die of the disease despite theconsiderable advances in treatment. According to the American CancerSociety, in 2007, an estimated 178,480 new cases and 40,460 deaths frombreast cancer in women are expected to occur in the United States.Breast cancer development in men is also increasing. Given thesecircumstances, early detection of breast cancer is considered animportant prognostic factor. Ideally, death from malignancy rather thanits lack of detection should be the point of reference in evaluating anyscreening program.

Breast cancer occurs when cells in the breast begin to grow out ofcontrol and invade nearby tissues or spread throughout the body. It isone of the leading causes of cancer death in women. Mammography is themost commonly used screening modality for the early detection of breastcancer. However, mammography is of limited value in young andpremenopausal women because denser breast tissue produces mammographicimages which are difficult if not impossible to interpret. Therefore,there is a need to develop novel and more effective screening strategieswith a high sensitivity and specificity.

Cancer development in tissue below the breast surface appears togenerate an increase in the temperature on the breast surface. Forseveral decades medical researchers around the world have struggled tofind an accurate method for interpreting thermal circadian data relatedto tumor growth in the breast, and using this as a detection modality.It is recognized that the breast exhibits a circadian rhythm that isreflective of its physiology. Areas of mammalian tissue adjacent tocarcinomas exhibit increased temperatures from that exhibited bynon-adjacent, non-cancerous areas. The temperature of a cancer-affectedarea can fluctuate several degrees Centigrade from normal tissue; thisdifference having been demonstrated while monitoring such an area for a24-hour period. The relationship between breast skin temperature andbreast cancer has been documented and it has been found that thedifferences between the characteristics of rhythmic changes in skintemperature of clinically healthy and cancerous breasts were real andmeasurable.

Currently mammography is considered the gold standard as a screeningtool for the early detection of breast cancer. Unfortunately, widevariations exist in its sensitivity and specificity in publishedreports. Mammographic sensitivity varied from 100% in fatty breasts to4% in extremely dense breasts, as evidenced by a recent study. As aconsequence, other technologies have been used in an effort tocomplement mammography. Magnetic resonance imaging (MRI) has been shownto be more sensitive in the early detection of occult breast cancers,particularly in pre-menopausal women for whom the sensitivity ofmammography is compromised, but with less specificity and greater cost.Additional modalities are still under development, such as electricalimpedance scanning (EIS), mammary ductoscopy (MD), and proteomics ofnipple aspirate fluid (NAF) and serum. In spite of these advances, womenin the United States are subjected to numerous unnecessary breastbiopsies each year because of the inadequacies of the aforementionedbreast cancer detection modalities' inability to separate benign fromcancerous lesions.

Recent reports indicate that MRI is able to detect cancer in thecontralateral breast even when such cancers were missed by mammographyor in clinical examination at the time of the initial breastexamination. In addition, MRI has been proven to be a better screeningtool for women with genetic mutations of the BRCA1 or BRCA2 genes, andin those women with a strong family history of breast cancer. Althoughthe sensitivity of MRI is better than that of mammography, the techniqueis flawed by a lower specificity and a far greater expense. However,recently, the American Cancer Society announced a change in its breastcancer screening recommendation guidelines, recommending that women withhigh genetic risk (such as those who have mutation in the BRCA1 or BRCA2genes or those with a strong family history of breast cancer) bescreened with magnetic resonance imaging.

An additional source of concern relates to the fact that radiologistsfail to detect cancer in up to thirty percent of patients with breastcancer despite the fact that the malignancies missed by the radiologistsare evident in two thirds of the mammograms. There is a need to furtherassist radiologists, surgeons and other physicians in detecting,diagnosing, successfully biopsing, and operating on precancerous andcancerous conditions.

The establishment and growth of most tumors depends on the successfulrecruitment of new blood vessels into and around the tumor cells. Thisprocess, also known as angiogenesis, is dependent on the production ofangiogenic growth factors by the tumor cells. Angiogenesis results in amore constant blood flow to the area of the tumor, which increases thelocal temperature in the area surrounding the tumor in comparison tonormal breast tissue.

The superficial thermal patterns measured on the surface of the breastare related to tissue metabolism and can serve as a means to visualizeactivity within the underlying tissue. Such thermal patterns changesignificantly as a result of normal phenomena including the menstrualcycle, pregnancy and, more importantly, the pathologic process itself.Cancer development, in most instances, represents the summation of alarge number of mutations that occur over years, each with its ownparticular histologic phenotype that can be seen in pre-menopausalmastectomy specimens. Cancer development appears to generate its ownthermal signatures, and the complexity or lack thereof may be areflection of its degree of development.

Thermographic technology was originally introduced to complementmammography because it was felt that a thermogram of the breast was ableto detect breast cancer development up to 10 years earlier than mostconventional modalities. However, the accuracy of thermography hasremained questionable due to a number of factors, such as the symmetryand stability of the breasts' temperature during the menstrual cycle andtemperature fluctuations caused by the use of oral contraception.

One prior device used for detecting cancer is a brassiere that includesa plurality of temperature sensors, an analog multiplexer circuit, acontrol circuit, a sample and hold circuit, an analog/digital converter,a buffer register, a storage register, a clock and a data logger. Thedevice allows for the storage of temperature readings in a digital form.This digital data may be uploaded to the data logger which converts thedigital signals to decimal form so that the temperature differences maybe read and analyzed by a supervising physician and the problemsassociated with such devices are stated in commonly owned U.S. Pat. No.6,389,305.

Other devices use a passive thermographic analytical apparatus thatprovide a direct readout of the results through analysis of athermographic radiation pattern of the human body. Such devices areunable to detect small tumors on the order of less than 0.5 cm andpossibly other larger tumors and certain types of cancers, and do nottake into account the chaotic fluctuation of normal body temperaturesover time and between locations on the body.

Many cancers are diagnosed too late; successful treatment is moreattainable if the cancer is found at early stages. Other devicesdescribed in U.S. Pat. Nos. 6,389,305, 5,941,832 and 5,301,681 have metwith some limited success, but are yet to provide an optimal breastcancer detection device. There remains a need to improve the method anddevice for detection of potentially cancerous conditions in breasts.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1A illustrates a front-view of the preferred embodiment of thepresent invention;

FIG. 1B illustrates a side view of the preferred embodiment of thepresent invention;

FIG. 1C illustrates a back view of the preferred embodiment of thepresent invention;

FIG. 2 illustrates the sensor placeholder in accordance with thepreferred embodiment of the present invention;

FIG. 3 illustrates a sensor placeholder positioned over the left breastand a second sensor placeholder positioned over the right breast, withthe sensors placed over the sensor placeholder;

FIG. 4 illustrates the relative positioning of the thermal sensors onthe left and right breasts of a subject in accordance with the preferredembodiment of the present invention;

FIG. 5 illustrates a feed-forward neural network in accordance with thepreferred embodiment of the present invention;

FIG. 6 illustrates temperature readings for three sensors plotted versustime, showing abnormal temperature readings; and

FIG. 7 illustrates a user interface for the temperature readingsanalysis and diagnosis in accordance with the preferred embodiment ofthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to a device and method for providingimproved classification of breast tissue as either suspect tissue ornon-suspect tissue. Suspect tissue can then be further analyzed using avariety of well-known diagnostic techniques. The device of the presentinvention includes a set of temperature sensors placed on the left andright breasts of a patient in accordance with a predefined pattern. Thesensors collect temperature readings over a predetermined period oftime. The collected temperature readings are then classified using oneor more classifier systems.

The concept behind the present invention is that new blood vesselssupplying a breast cancer do not respond to normal physiological controlmechanisms (vasoconstriction and vasodilatation). Therefore, suspecttissue, such as cancers produce time-related pattern changes not seen innormal tissue. These pattern changes have been detected over a circadian(24 hour) rhythm, but can be detected over shorter periods of time, aslittle as three hours. The present invention views these chaotic changesin the circadian rhythm, or shorter time periods, as a signal of highrisk for breast cancer in a tested subject even in the absence of othermammographic evidence, such as an x-ray.

A classifier system is an algorithm that assigns a class label to anobject or set of data based on the description and characteristics ofthe objects or the set of data. A classifier system accepts data as oneor more inputs, and outputs the best possible action based on thecharacteristics of the input data. A classifier system is first trainedusing a set of data. This allows the classifier system to learn a set ofcharacteristics about the data. For example, an above averagetemperature in the breasts could signify the presence of canceroustissue.

A classifier system could be trained using supervised learning,unsupervised learning, reinforcement learning, or a combination of theselearning methods. In supervised learning, the training data consists ofa set of one or more inputs and a set of expected or desired outputsassociated with each input. In unsupervised learning the inputs consistsof unlabeled data and the goal is to determine how the data areorganized. A form of unsupervised learning is clustering. Inreinforcement learning the goal is to maximize some notion of long-termreward in response to a set of one or more inputs. In the preferredembodiment of the present invention, the classifier systems would betrained using supervised learning. Nevertheless, the classifier systemscould be trained using other methods including unsupervised learning orreinforcement learning.

The training data could consist of temperature readings with aboveaverage temperatures, enabling the classifier system to learn toassociate above average temperatures with suspect or cancerous tissue.In addition, the training data could consist of examples ofnon-cancerous patients in the form of average temperature readings.Additional training data could consist of anomalous temperature readingsthat change over the test period. For example, a contusion may have anelevated temperature for an initial period of time, but cool off as thecontusion heals, or a sensor may loose contact with the skin of thesubject for a period to time and indicate a lower than averagetemperature during that period, or a subject may touch a sensor during atemperature reading causing a higher than average reading for a singlereading. This training data would enable the classifier system to learnto associate average temperature readings and temperature readings thatdecrease over time with non-cancerous tissue.

The output of the classifier system could vary depending on the numberof classes needed. For example, if the only two classes of interest arecancerous and non-cancerous, then the output of the classifier systemcould be a single number, with a value of 0 denoting cancerous tissueand a value of 1 denoting non-cancerous tissue. If the data needed to befurther classified as easy to detect or hard to detect, then anothernumber could be output by the classifier system, with 0 denoting easy todetect and 1 denoting hard to detect. Under such a system, for example,an output of two zeros could signify cancerous tissue that is easy todetect.

There are many classifier systems that could be used and that are wellknown in the art. Further, methods used for the design, training, andtesting of classifier systems are numerous. Thus, it is not possible,realistic or necessary to attempt to explain all of those methods inthis description. Moreover, it is not necessary to describe such methodsherein because anyone of skill in the art of the present invention willbe able to implement such functionality without undue experimentation.Nevertheless, a number of different types of classifier systems will bedescribed below.

Once the classifier system reaches a desirable level of performancethrough training, by correctly displaying the correct output given knowninput data, the classifier is then tested against a second set of data.The testing against this other set of data is done to check how well theclassifier system performs on data it has not been exposed to duringtraining, hence its ability to learn characteristics from the trainingdata.

In the preferred embodiment of the present invention, a neural networkwould be used as the classifier system. The neural network could betrained using a variety of methods, such as with back-propagation orneuroevolution. There are many supervised and unsupervised learningtechniques for neural networks known in the art.

In an alternative embodiment, an ensemble of classifier systems would beused for classification of the temperature data. The ensemble ofclassifier systems could consist of the same type of classifier systemor consist of different types of classifier systems. For example, anensemble of neural networks could be used to classify the temperaturedata of a subject, with each neural network from the ensemble havingbeen initialized with different node weights, or each neural networkhaving a different topology, or each neural network using differentactivation functions, or each network having been trained using adifferent set of training data. Alternatively, the ensemble couldconsist of different types of classifiers, such as having onefeed-forward neural network, one recurrent neural network, one decisiontree, and one support vector machine. When using an ensemble ofclassifier systems, the output of each classifier system could behandled in different ways to present a diagnosis based on thetemperature data. In an embodiment, the most frequent diagnosis from theensemble of classifier systems is given as the final diagnosis.Alternatively, a weight could be associated with different classifiersystems, with the output of each classifier system then given acontribution to the final diagnosis relative to its weight value.

In yet another embodiment, a single classifier system is selected from agroup comprising of a neural network, a radial basis function, aGaussian mixture model, a fuzzy network, and a support vector machine.In an embodiment, a user could pick the specific type of classifiersystem to use for the classification diagnosis. In another embodiment,all of the classifier systems would be used for the classificationdiagnosis. Because the performance of the various classifier systems mayvary, the diagnosis received by a majority of the classifier systemswould then be used as the classification diagnosis. Alternatively, aprobability for a certain diagnosis could be presented by taking thenumber of cancerous or suspect diagnosis divided by the total number ofdiagnosis. For example, if two out of five classifiers gave a suspectdiagnosis, then the system could present the user with a message thatstates that the user has a 20% chance of currently having cancer orneeding consultation to identify the reason for the suspect diagnosis.In yet another embodiment, the specific classifier system to use couldbe determined based on the subject's demographic and medicalinformation. For example, if the subject is post-menopausal, then asupport vector machine could be used as the classifier system. If thesubject is under 30 years old, then a neural network could be usedinstead.

The present invention collects data from a plurality of temperaturesensors affixed to the breasts of a subject. The sensors collecttemperature data from surrounding tissue of the breasts at regular timeintervals, such as every five minutes, over a period of time, such asbetween three and 24 hours. All of the generated temperature data isstored in a portable computing or storage device. The heads of thesensors are placed on a sensor placeholder, a sensor placeholder beingplaced on each breast, if both breasts are being tested. In thepreferred embodiment of the present invention the portable computingdevice would consist of a microprocessor and storage unit attached to 16thermal sensors. In a presently preferred embodiment, the thermalsensors are manufactured by YELLOW SPRINGS INSTRUMENT COMPANY. Themicroprocessor and storage unit would be manufactured by LIFELINEBIOTECHNOLOGIES.

An alternative portable computing device or a storage system could beused, as long as a suitable interface is provided for receiving thetemperature readings from the thermal sensors and subsequently recordingand storing these in the storage system or in the portable computingdevice. For example, the sensors could be connected to a wirelesstransmitter that transmits the temperature readings as they are taken toa remote storage system, for subsequent analysis, or directly to ananalysis system for real-time processing. The portable computing devicecould also include a clip that would allow for the device to be attachedto clothing during the data collection period.

In the preferred embodiment, the patient would wear the portablecomputing device with the sensors for a total of 24 hours. However, thetime period could also be varied, such as 12 hours, six hours, etc. Theportable computing device would collect data from the sensors every fiveminutes, but this time interval could be increased or decreaseddepending on the patient's medical history, the need for more data, orother medical factors. Once the data collection period ends, the datastored in the portable computing device would be transferred to acomputer, where the user would then be able to do the classificationanalysis with the classifier systems using a desktop application.Alternatively, the data could be uploaded to a website, and theclassification analysis could be done over a web interface. If theportable computing device was wirelessly enabled or with a physicalwire, such as an Ethernet cable, then the user could directly upload thedata to a website or other remote server from the portable computingdevice, where it could be accessed later for classifier analysis. In yetanother embodiment, the portable computing device would include adisplay, and the subject would be able to run the thermal data analysison the portable computing device and view the results on the display.

In the preferred embodiment of the present invention, a total of 16sensors would be used to collect temperature measurements of thebreasts, eight sensors placed on the right breast and eight sensorsplaced on the left breast. The sensors would consist of thermistors,whose use as temperature sensors is well known in the art. Throughoutthe remainder of this detailed description, the terms 1L-8L will be usedto refer to one or more of the sensors or thermistors on the leftbreast, while the terms 1R-8R will be used to refer to one or more ofthe sensors or thermistors on the right breast.

FIG. 1A illustrates a front view of the preferred embodiment of theportable device or microprocessor/storage system 100, the systemincluding temperature sensors used to collect temperature readings ofthe breast tissue of the subject. The body 102 of the system 100 wouldhold the microprocessor or storage system. The system 100 would includea detachable head 104, the detachable head 104 holding the sensor leadsor wires 108 and including an interface to connect with the system body102. Each sensor wire 108 would include a thermistor or temperaturesensor 112 at the end of the wire 108. The sensor wire 108 would becolor coded, with each of the eight sensor wires 108 on the right colorcoded the same as the corresponding eight sensor wires 108 on the left.For example, sensor wire 1L and sensor wire 1R would be coded the samecolor, 2L and 2R would be coded a different color, etc. The detachablehead 104 would include a label 106, with the label specifying thenumbering of each sensor 112 for the right breast and the numbering ofeach sensor 112 for the left breast.

FIG. 1B illustrates a side view of the system 100. The system 100 couldinclude a clip 114 enabling the device to be attached to clothing. FIG.1C illustrates a back view of the system 100. The hardware interface 116would be located between the detachable head 104 and the body 102. Thetype of hardware interface 116 could vary depending on the type ofmicrocontroller used in the body 102 or based on the type of system 100used. The system would be powered by a battery, housed under theremovable cover 118.

In the preferred embodiment of the present invention, the sensors 112are placed on the greatest areas of interest on the breast, based onwhere most breast cancers develop. A sensor placeholder 200, illustratedin FIG. 2, is utilized to facilitate the correct positioning of thesensors on the breast of the subject. The sensor placeholder 200 ispreferably lobate shaped with a set of four appendages 202, 204, 206,and 208. Although different shapes could be utilized, as long as suchshapes facilitated the placement of sensors on the areas of the breastmost prone to cancer, the lobate shape of the sensor placeholder 200 ispreferred because it aligns with the glandular regions of the breastswhere cancers are most likely to develop and insures proper sensorplacement. Each appendage of placeholder 200 preferably includes 11small holes 212 covering the length of each appendage and ending nearthe center hole 210, although a different number of holes could beutilized. The center hole 210 of the sensor placeholder 200 would beplaced over the nipple of the subject to align the sensor placeholder200.

A sensor placeholder 200 would be placed on each breast as illustratedby the partially broken depiction of FIG. 3. The temperature sensors 112would be placed in different holes 212 formed by each of the appendages202, 204, 206, and 208 of the two sensor placeholders 200. The locationof the temperature sensors 112 on each of the appendages, and the numberof sensors to put on each appendage, will depend on the size of thebreast, and any other medical information, such as patient age, statusof menopause, the results of previous breast examinations, etc. FIG. 3illustrates two sensor placeholders 200 placed over the breasts of asubject, with two sensors 112 placed on each appendage of the sensorplaceholder position over the right breast of the subject. Thepositioning of the sensors 112 in FIG. 3 is merely illustrative and doesnot designate preferred locations for the sensors 112, which are furtherdiscussed with reference to FIG. 4 below. Although not shown in FIG. 3,which presents the wires 108 for the left breast as partially broken,the sensors 112 would also be placed in similar locations on the sensorplaceholder positioned over the subject's left breast.

FIG. 4 illustrates the relative positioning of the sensors on thebreasts of a subject, based on the most likely occurrence of cancers, inaccordance with the preferred embodiment. The thermistors 1L-8L and1R-8R would be placed on the breasts as follows: 1L and 1R below thenipple; 2L and 2R in the upper outer quadrant; 3L and 3R in the upperouter quadrant toward the axilla; 4L and 4R on the upper areola; 5L and5R on the vertical midline above the horizontal midline; 6L and 6R inthe upper inner quadrant; 7L in an ambient temperature zone; 7R on thesternum; 8L and 8R in a flexible position, such as an area of concern,near the position of a palpable lesion, or at contralateral positions.Each pair of the thermistors (such as 1L/1R, 2L/2R, etc.) is preferablymarked to allow for easy identification of each thermistor pair as wellas each thermistor. For example, as noted above, each thermistor paircould be color coded and tabbed with a number and letter. Eachthermistor and its signals are consequently identified with a specificposition on the breast.

The labeling of the data generated by each thermistor simplifiessubsequent processing and improves accuracy of the signals in terms ofindividual signal correlation with calibration data and selection ofspecific signal sources for manipulation in developing thegeneralization of physiological condition. This also simplifiescorrelation of results with specific sensor positions on the breast toarrive at a more specific determination of the location of abnormalphysiological conditions. While the number of thermistors andpositioning are specifically set forth, it is conceived that accuracyincreases as the number of thermistors increases. The eight sensorsplaced on each breast would be placed in the areas of the breast wherethe greatest number of breast cancers form, as generally illustrated inFIG. 4.

In the preferred embodiment of the present invention, the temperaturereadings for the patient would be normalized to a common range, such asthe range of 0 to 1. These temperature readings would then be analyzedby the classifier systems. The temperature readings would serve as theinput to the classifier systems, and the output of the classifiersystems would be a signal or number signifying either a suspect ornon-suspect result. While generally a suspect result would be acancerous one, elevated temperature readings could be due to a number offactors. Many times, the output of the classifier will be such that avery reasonable determination regarding the presence of cancer can bemade, but at others, it may be more difficult, so before a potentialcancer determination is made, additional testing of suspect tissue maybe required.

Normalization of the temperature readings allows for easy comparison ofthe temperature readings from one or more users. The result ofnormalization is a data set within the same range, typically from 0 to1, but any data set can be normalized to any other data range. Forexample, the data could also be normalized to a number from 0 to 100.Normalization of the temperature data is done by taking each temperaturereading and dividing it by the maximum temperature reading in thetemperature data. This will result in all numbers being in the rangebetween 0 and 1. Once all numbers have been converted to the 0 to 1range, they can be further converted to other number ranges asnecessary, such as 0 to 100, −100 to 100, 25 to 50, etc. For example, ifthe maximum temperature reading in all of the collected temperature datafor all users is 38 Celsius, then the temperature reading of aparticular subject of 32 Celsius would be equal to 32 divided by 38,which is equal to 0.842. If the temperature reading of the particularsubject had been equal to 38 Celsius, then the normalized temperaturereading would have been equal to 1. Normalization methods are well knownin the art.

Outliers in the temperature readings would represent abnormaltemperature readings. For example, a subject may be touching a sensor112 at the time a temperature reading is obtained, or driving in a carwith the sun shining on the subject's left breast, but not the rightbreast. Either of these situations may result in one or more temperaturereadings that are abnormally high and outside of the range oftemperature readings that were taken in the absence of such abnormalcircumstances. Such abnormalities can be identified by measuring thedifference between any two temperature readings from the sensors at apoint in time. Fluctuations in the temperature readings are expected,but any fluctuations beyond a threshold value would be flagged asabnormal readings. Outliers or other anomalous temperature readingswould preferably be filtered out by the classifier system.

In the preferred embodiment of the present invention, FIG. 5 depicts afeed-forward neural network used for the classification of the fourclasses of normal, benign, cancer, and suspected cancer. Thefeed-forward neural network would be trained using back-propagation.Back propagation was created by generalizing the Widrow-Hoff learningrule to a multiple layer network and nonlinear differentiable transferfunction. Input vectors and corresponding target vectors are used totrain a network until it can approximate a function, by associatinginput vectors with specific output vectors, or classify input vectors inan appropriate way. Networks with biases, a sigmoid layer and a linearoutput layer are capable of approximating any function with a finitenumber of discontinuities. In an embodiment, a recurrent neural networkwould be used in place of the feed-forward neural network. In afeed-forward neural network, the output of every node is connected tonodes in the next layer. A recurrent neural network allows for theoutput of a node to be connected to a node in the next layer, a node inthe previous layer, to itself, or to a different node in the currentlayer. These recurrent connections create an internal state of thenetwork that allows the network to exhibit dynamic temporal behavior,thus allowing the neural network to retain information betweenobservations.

Neural networks typically have one or more hidden layers of sigmoidneurons followed by an output layer of linear neurons. Multiple layersof neurons with nonlinear transfer functions allow the network to learnnonlinear and linear relationships between input and output vectors. Thelinear output layer allows the network to produce values outside therange −1 to +1.

Before training a neural network, the weight and biases must beinitialized. Random numbers around zero were used to initialize weightsand biases in the network. The training process requires a set of properinputs and targets as outputs. During training, the weights and biasesof the network are iteratively adjusted to minimize the networkperformance function. The default performance functions for feed-forwardnetworks are the mean square errors, the average squared errors betweenthe network outputs and the target output.

The weight update aims at maximizing the rate of error reduction. Theweight increment is done in small steps; the step size is chosenheuristically, as there is no definite rule for its selection. In thepresent case, a learning constant η equal to 0.9 (which controls thestep size) was chosen by trial and error. While the neural network ispresented with a preferred topology, the present invention is notlimited to a neural network with such topology. Other neural networkswith different topologies, or with recurrent connections, could also beused. The manner in which the neural network, and the other classifiersystems, are trained, and any training parameters such as the learningconstant, the range of values used to initialize the weights, and thenumber of iterations needed may all be varied and still be within thescope of the present invention.

An embodiment of the neural network structure or topology is shown inFIG. 5. It consists of 16 input nodes to accept the data from the 16sensor 112 readings at a point in time, two hidden layers with 17neurons each, and an output layer with four nodes. The neural networkshown in FIG. 5 is fully connected, meaning that every node is connectedto every node in the next layer. For example, a node in the input layerwould be connected to each of the 17 nodes in the first hidden layer,and each node in the first hidden layer is connected to each of the 17nodes in the second hidden layer.

The four nodes in the output layer are used to identify the four classesof normal, benign, cancer, and suspected cancer. For example, an outputof 0001 could be obtained by having the first output node with a valueof 0, the second output node with a value of 0, the third output nodewith a value of 0, and the fourth output node with a value of 1. Thecode 0001 could represent that the input temperature readings should beclassified as normal. The other three classes could be classified byhaving an output of 0010 represent benign, an output of 0100 representcancer, and 1000 represent suspected cancer. Regardless of the type ofnumeric output used, these output numbers could be converted to a textform or as a label when the diagnosis based on the classificationresults is presented.

In another embodiment of the present invention, the neural network couldconsist of a single hidden layer, or the number of nodes on the one ormore hidden layers could be equal or less than the number of nodes inthe input layer. Further, the neural network does not have to be fullyconnected. In another embodiment a node would be connected to only halfof the nodes in the next layer. Alternatively, the neural network couldinclude recurrent connections, so that the output of a node is not onlypropagated forward, but also connected back to a node in the previouslayer or connected back to a node in the current layer.

Another classifier system that could be used is a radial basis function(RBF) network. RBF networks have a static Gaussian function as thenonlinearity for the hidden layer processing elements. The Gaussianfunction responds only to a small region of the input space where theGaussian is centered. The key to a successful implementation of thesenetworks is to find suitable centers for the Gaussian functions. Thisaction can be done with supervised learning, but an unsupervisedapproach usually produces better results.

The simulation starts with the training of an unsupervised layer. Itsfunction is to derive the Gaussian centers and the widths from the inputdata. These centers are encoded within the weights of the unsupervisedlayer using competitive learning. During the unsupervised learning, thewidths of the Gaussians are computed based on the centers of theirneighbors. The output of this layer is derived from the input dataweighted by a Gaussian mixture.

Once the unsupervised layer has completed its training, the supervisedsegment then sets the centers of Gaussian functions (based on theweights of the unsupervised layer) and determines the width (standarddeviation) of each Gaussian. Any supervised topology (such as amulti-layer perceptron) may be used for the classification of theweighted input.

The advantage of the radial basis function network is that it finds theinput to the output map using local approximators. Usually thesupervised segment is simply a linear combination of the approximators.Since linear combiners have few weights, these networks train extremelyfast and require fewer training samples.

A key to classifier systems is their ability to learn nonlinearrelationships between the input data and the target data. Nonlinearityis necessary to describe complex phenomena, since a simple linearrelationship is not always sufficient to approximate complex systems orsets of data. A linear system is a problem where the variables to besolved for cannot be written as a linear combination of independentcomponents. Most physical systems are nonlinear in nature, hence theneed for systems that can learn nonlinear relationships.

Yet another classifier system that could be used is a fuzzy network. Ina fuzzy network-based classifier the pattern space is divided intomultiple subspaces. For each of these subspaces, the relationshipsbetween the target patterns and their classes are described by if-thentype fuzzy rules. For example, if temperature readings are above 36Celsius for 12 consecutive hours, then the class is cancer; or if thetemperature readings are above 37 Celsius no more than three times for aperiod of 12 hours, then the class is normal.

The advantage of this system is that a nonlinear classification boundarycan be easily implemented. Unknown patterns are classified by fuzzyinference, and patterns that belong to an unknown class, which is notconsidered during the training phase, can be easily rejected. A simplelearning procedure and a genetic algorithm can be utilized, as is knownin the art, to acquire a fuzzy classification system automatically.These methods divide the pattern space into a lattice-like structure.Therefore, many fuzzy rules corresponding to fine subspaces may benecessary to implement a complicated classification boundary.

A fuzzy classifier of the type known in the art could be implemented asfollows. The first step is to fuzzify the inputs, in this case the 16sensor readings at a point in time. The inputs are fuzzified using asymmetric Gaussian membership function given by:

${f\left( {{x;\sigma},\mu} \right)} = \frac{{\mathbb{e}}^{- {({x - \mu})}^{2}}}{2\sigma^{2}}$

where σ and μ are variance and mean respectively. The second stepconsists of fuzzy inference. Fuzzy inference is the process offormulating the mapping from a given input to an output using fuzzylogic for making decisions. From the fuzzified inputs, the clustercenters are determined using a subtractive clustering method. In thesubtractive clustering method, the data point with the highest potentialto be the first cluster center is selected. All data points in thevicinity of the first cluster center, as determined by a radius, areremoved in order to determine the next data cluster and its centerlocation. This process is iterated until all of the data is within theradius of a cluster center.

The third step is obtaining the membership computation. The final outputis obtained using the Sugeno fuzzy model. The output membership functionis linear and is given by r=ax+by+cz+d, where a, b, c, d are theadaptive parameters. The output level r_(i) of each rule is weighted bythe firing strength w_(i) of the rule. The final output of the system isthe weighted average of all rule outputs and is computed as:

${{Final}\mspace{14mu}{Output}} = \frac{\sum\limits_{i = 1}^{N}\;{w_{i}r_{i}}}{\sum\limits_{i = 1}^{N}\; w_{i}}$

where N is equal to the total number of fuzzy rules.

A Gaussian Mixture Model (GMM) could also be used as a classifiersystem. A GMM is a parametric model used to estimate a continuousprobability density function from a set of multi-dimensional featureobservations. It is widely used in data mining, pattern recognition,machine learning and statistical analysis. This Gaussian mixturedistribution can be described as a linear superposition of Kmultidimensional Gaussian components given by:

${p(x)} = {\sum\limits_{k = 1}^{K}\;{\pi_{k}{N\left( {\left. x \middle| \mu_{k} \right.,\Sigma_{k}} \right)}}}$

where π_(k), μ_(k), Σ_(k) are mixing coefficients, mean, and covariancerespectively.

The solution for determining the parameters of GMM is estimated by usingthe maximum likelihood (ML) criterion. A powerful method for maximizingthe likelihood solution models is by the general form ofExpectation-Maximization (EM) algorithm. To carry out the EM algorithm,first the means, covariances, and mixing coefficients are initializedand the initial value of the log likelihood is evaluated. The E step ofthe EM algorithm evaluates the responsibilities using the currentparameter values as follows:

${y\left( z_{nk} \right)} = \frac{\pi_{k}{N\left( {\left. x_{n} \middle| \mu_{k} \right.,\Sigma_{k}} \right)}}{\sum\limits_{j = 1}^{K}\;{\pi_{j}{N\left( {\left. x_{n} \middle| \mu_{j} \right.,\Sigma_{j}} \right)}}}$

The M step of the EM algorithm re-estimates the parameters using thecurrent responsibilities as follows:

$\mu_{k}^{new} = {\frac{1}{N_{k}}{\sum\limits_{n = 1}^{N}\;{{y\left( z_{nk} \right)}x_{n}}}}$

$\sum\limits_{k}^{new}\;{= {\frac{1}{N_{k}}{\sum\limits_{n = 1}^{N}\;{{y\left( z_{nk} \right)}\left( {x_{n} - \mu_{k}^{new}} \right)\left( {x_{n} - \mu_{k}^{new}} \right)^{T}}}}}$$\pi_{k}^{new} = \frac{N_{k}}{N}$ where$N_{k} = {\sum\limits_{n = 1}^{N}\;{y\left( z_{nk} \right)}}$

The last step is to evaluate the log likelihood and check forconvergence of either the parameters or the log likelihood. If theconvergence criterion is not satisfied, then the process returns to theE step and the process repeats. The log likelihood is computed asfollows:

${\ln\left( {p\left( {\left. X \middle| \mu \right.,\Sigma,\pi} \right)} \right)} = {\sum\limits_{n = 1}^{N}\;{\ln\left\{ {\sum\limits_{k = 1}^{K}\;{\pi_{k}{N\left( {\left. x_{n} \middle| \mu_{k} \right.,\Sigma_{k}} \right)}}} \right\}}}$

The EM algorithm attempts to find the centers of natural clusters in aset of data. The EM algorithm takes more iterations to reach convergencecompared with the K-means algorithm, another popular algorithm used instatistics and machine learning for cluster analysis. Hence, it iscommon to use the K-means algorithm to find the initial estimates of theparameters obtained from a sample of the training data. The K-meansalgorithm uses the squared Euclidean distance as the measure ofdissimilarity between a data point and a prototype vector. This not onlylimits the type of data variables to be considered but also makes thedetermination of the cluster means non-robust to outliers. Thisalgorithm starts off by choosing randomly the initial means and assumedunit variances for the diagonal covariance matrix which is being adaptedin the present invention.

One of the important attributes of the GMM is its ability to form smoothapproximations for any arbitrarily-shaped densities. As real-world datahas multi-modal distributions, GMM provides an extremely useful tool tomodel the characteristics of the data. Another similar property of GMMis the possibility of employing a diagonal covariance matrix instead ofa full covariance matrix. Thus, the amount of computational time andcomplexity can be reduced significantly. GMMs have been widely used inmany areas of pattern recognition and classification, with great successin the area of speaker/voice identification and verification.

Yet another classifier system that could be used in the presentinvention is a support vector machine (SVM). SVMs are known as the“nonparametric” model in which parameters that define the capacity ofthe model are data-driven in such a way as to match the model capacityto the data complexity. It is developed in reverse order compared to thedevelopment of neural networks, as the value of the training error isbeing fixed and the confidence interval is minimized.

The SVM is a supervised learning method that generates input-outputmapping functions from a set of labeled training data. The mappingfunction can be either a classification function or a regressionfunction. For classification, nonlinear kernel functions are often usedto transform input data to a high-dimensional feature space in which theinput data become more separable compared to the original input space.Maximum-margin hyper-planes are then created; hence, the model produceddepends on only a subset of the training data near the class boundaries.This classification method is currently adopted in the presentinvention.

The aim of SVM modeling is to find a separating hyperplane whichseparates positive and negative examples from each other with optimalmargin; in other words, the distance of the decision surface and theclosest example is maximal. Essentially, this involves orienting theseparating hyperplane to be perpendicular to the shortest lineseparating the convex hulls of the training data for each class, andlocating it midway along this line. The vectors that constrain the widthof the margin are the support vectors.

Let the separating hyperplane be defined by x·w+b=0 where w is itsnormal. For linearly separable data labeled {x_(i), y_(i)}, x_(i)ε

^(p), y_(i)ε{−1,1}, for i=1, . . . N, the optimum boundary chosen withmaximal margin criterion is found by minimizing the objective function:E=∥w∥ ²subject to (x _(i) ·w+b)y _(i)≧1, for all i.  (1)

The solution for the optimum boundary w₀ is a linear combination of asubset of the training data, sε{1 . . . N}, the support vectors. Thesesupport vectors define the margin edges and satisfy the equality(x_(i)·w+b)y_(s)=1. Data may be classified by computing the sign ofx·w₀+b, with a positive sign denoting a first type of class and anegative sign denoting second type of class.

Generally, the data are not separable, and the inequality in theequation (1) cannot be satisfied. In this case, a “slack” variable ξ_(i)can be used that represents the amount by which each point ismisclassified. The new objective function is now reformulated as

$\begin{matrix}{{E = {{\frac{1}{2}{w}^{2}} + {c{\sum\limits_{i}\;{L\left( \xi_{i} \right)}}}}}{{{{subject}\mspace{14mu}{to}\mspace{14mu}\left( {{x_{i} \cdot w} + b} \right)y_{i}} \geq {1 - \xi_{i}}},{{for}\mspace{14mu}{all}\mspace{14mu}{i.}}}} & (1)\end{matrix}$

The second term on the right-hand side of equation (2) is the empiricalrisk associated with those points that are misclassified or lie withinthe margin. L is a cost function and C is a hyper-parameter thattrades-off the effects of minimizing the empirical risk againstmaximizing the margin. The first term can be thought of as aregularization term, deriving from maximizing the margin, which givesthe SVM its ability to generalize well on sparse training data.

Kernel functions can be used to resolve nonlinear boundary problems.Kernel functions define a nonlinear mapping from the input space(observed data) to a manifold in higher dimensional feature space, whichis defined implicitly by the kernel functions. The hyperplane isconstructed in the feature space and intersects with the manifold,creating a nonlinear boundary in the input space. In practice, themapping is achieved by replacing the value of the dot products betweentwo vectors in the input space with the value that results when the samedot product is carried out in the feature space. The dot product in thefeature space is expressed by functions (i.e., the kernels) of twovectors in input space. The polynomial and radial basis function kernelsare commonly used and they are:

K(x_(i), x_(j)) = (x_(i) ⋅ x_(j) + 1)^(n) and${K\left( {x_{i},x_{j}} \right)} = {\exp\left\lbrack {{- \frac{1}{2}}\left( \frac{{x_{i} - x_{j}}}{\sigma} \right)^{2}} \right\rbrack}$

Respectively, where n is the order of the polynomial and a is the widthof the radial basis function, the dual for the nonlinear case is givenby:

$\alpha^{*} = {\max\limits_{\alpha}\left( {{\sum\limits_{i}\;\alpha_{i}} + {\sum\limits_{i,j}\;{\alpha_{i}\alpha_{j}y_{i}y_{j}{K\left( {x_{i} \cdot x_{j}} \right)}}}} \right)}$

Subject to 0≦α_(i)≦C, Σ_(i)α_(i)y_(i)=0. With the above formulation onthe use of kernels, an explicit transformation of the data to thefeature space is not required. Several algorithms extend the basicbinary SVM classifier to a multi-class classifier. Examples consistingof one-against-one SVM, one-against-all SVM, half-against-half SVM, anddirected acyclic graph SVM.

In an embodiment of the present invention, the readings at a point intime from all 16 sensors would be stored as a subset of the temperaturereadings or a line in a file, with the file containing the readings fromall sensors for the monitoring period of time. Alternatively, thereadings from the 16 sensors can be grouped together using a delimiter,in order to identify the readings from the 16 sensors at differentpoints in time. Abnormalities in temperature readings would beidentified by comparing the readings from any two sensors at a point intime. For example, the temperature reading at time T of sensor 1L wouldbe compared to the temperature reading of sensors 2L-8L and 1R-8R. Ifthe difference between the readings of any two sensors is greater thanthree degrees, or some other threshold value, then the data is marked asan abnormality and ignored for data processing.

A confidential user study was conducted in order to test the preferredembodiment of the present invention. Examinations for each patient weredone concurrently on contralateral areas on both breasts under closemonitoring for a specific period of time. FIG. 4 illustrates how thesensors were placed on the surface of the breasts of the patients thatparticipated in the user study.

Patient information was collected, compiled, and separately documentedto serve as benchmarks for later comparison of the performance of thepresent invention with the actual diagnosis of the study patients. Theinformation collected included mammogram results, such as suspicion ofcancer or benign tumors, the size of the tumor in millimeters; andbiopsy results such as ductal carcinoma in-situ, invasive carcinoma,hyperplasia or cysts, among others. Other patient information collectedincluded patient age, status of menopause, etc.

The temperature readings collected from the study participants wereclassified according to the results of the patients' biopsy results anddiagnosis. The data was arranged in two separate files, one filecontaining data classified as benign and the other file containing dataclassified as suspect or cancer. These two files were subsequentlydivided into “easy to detect” and “difficult to detect”, based on boththe biopsy results and the location of the lesion. The definition foreasy to detect was that the sensors 112 line up with the location of thelesion and the biopsy result. The definition for difficult to detect wasthat the sensors 112 line up with the location of the lesion, but do notmatch with the biopsy result.

The temperature reading data collected from the study patients wasclassified according to the results of the patients' illness. The twomain categories are namely benign and cancer. These are further dividedinto easy benign, difficult benign, easy cancer, and difficult cancer.

Due to the need for greater clarity, accuracy and ease of understandingthe results from the study, a total of four different classificationswere used to assess the collected temperature reading data. Theclassifications were normal breast, benign lesion, cancerous lesion, andsuspected cancer.

A total of 185 patients were evaluated using the present invention. Someof these patients were excluded from the analysis due to incompleteinformation, such as pending biopsy results and incomplete mammographyresults or incomplete temperature readings files. After compilation,there were a total of 90 patients involved in the data analysis. Table 1shows the data from 90 patients categorized under the four differentdiagnoses groups.

Number of temperature Number of Diagnosis data sets patients Normal 15003 Benign 1500 31 Cancer 1500 33 Suspected 1500 23 Cancer

The 1500 sets of temperature data were randomly selected from differentpatients belonging to the same group. Each set of the temperature dataconsisted of sixteen temperature measurements collected concurrently bythe sixteen sensors over the test period. There were a total of 6000sets of temperature readings used. These were further sorted and dividedinto two groups: 5000 sets to train each of the artificial classifiersystems with the number of learning iterations being about one millionper classifier, and the remaining 1000 sets were used as test data, totest the performance of the trained classifier systems. It was necessaryto have more training data than testing data to allow for bettertraining of the classifier systems.

Data inspections were required to ensure that temperature readings wereclean of all extraneous noise and abnormalities. Inspection was donethrough graphical analyses by converting all the sets of temperaturereadings from each patient into line plots or line charts. For eachpatient, the temperature from each of the sixteen sensors was plotted onthe same graph, with each sensor denoted by a different color. If anyabnormalities were found, such as great fluctuations of the temperatureon a particular sensor, the patient's data would be excluded from theanalysis. Examples of such abnormalities are illustrated in FIG. 6. Thethree lines 602, 604 and 606 on FIG. 6 represent the temperaturereadings received during the various time for three out of 16 sensors.The temperature readings are expected to vary within a reasonable range.This is the case for line 602 representing one sensor from the graph onFIG. 6, which remained relatively level. The other two lines, 604 and606, represent two sensors that show drastic drops in temperatures fromone time period to the next. The potential causes for such phenomenainclude poor contact between the sensors and the breast surface,continuous data recording after the screening system has shut off, andsensors dropped off from the breast surface in the midst of temperaturerecording.

There are several ways to address such temperature abnormalities duringdata collection. In one embodiment of the present invention, thetemperature reading data that has the lowest temperature is manuallyremoved by the subject during the data collection phase. In yet anotherembodiment, an alarm would be placed on each sensor to detectabnormality during monitoring and data collection. The alarm could thendiscard the data without even storing it. Alternatively, the alarm couldtrigger a visual or an audial cue that informs the subject to readjustthe troubled sensor or sensors. In an alternative embodiment, all of thedata would be collected, but the data would go through a data cleaningprocess before data analysis. The data cleaning process would identifytemperatures that are outside of a reasonable range and wouldsubsequently delete the corresponding data. A statistical analysis couldbe used for the data cleaning, with outlier temperature readings beingdeleted. Yet another solution would be to apply a regression approach toselect the best appropriate input data for the training and testing ofthe classifier systems.

A key aspect of the present invention is normalization of thetemperature readings. Each set of temperature readings has its owntemperature range, depending on each individual's health and bodyconditions. For example, some patients could have body temperatureranges from 30 to 35 Celsius, while others could range from 32 to 36Celsius. In addition, temperature ranges could vary for a particularpatient at different times of the day. Further, the temperature of onebreast could normally be different from the temperature of the otherbreast.

The classifier systems are trained by presenting the training data overseveral iterations. The number of iterations can be a fixed number, suchas 1000 iterations, or it can depend on the classifier system reachingan acceptable level of performance, such as classifying data correctlywith at least 80% accuracy. The system used for the user study wassufficiently trained after about 5000 iterations.

Five classifiers were tested during the user study: a feed-forwardneural network trained with back-propagation (BPA), a radial basisfunction, a fuzzy network, a Gaussian mixture model (GMM), and a supportvector machine (SVM). In testing the five classifiers, at least 1000sets of test data were used to compare the performance of theclassifiers. The following table shows the performance of the fiveclassifiers used for classification.

No. of No. of Percentage Type of training testing of correct classifiersdata used data used classification BPA 5000 1000 83.1 RBF 5000 1000 86.1Fuzzy 5000 1000 77.4 GMM 5000 1000 90.6 SVM 5000 1000 85.6 AVERAGE 84.5

All five classifiers managed to obtain approximately 85% of correctclassification. BPA classifier was trained using 2 hidden layers of 17neurons each. As noted above, FIG. 5 shows such a neural network withfour layers.

The BPA classifier was only able to classify the unknown data correctlywith an accuracy of 83.1%. Among these five classifiers, GMM had thebest performance, as it had obtained the highest percentage of correctclassification of 90.6%, whereas RBF, Fuzzy, and SVM obtained 86.1%,77.4%, and 85.6% of accuracy respectively. The performance of the fiveclassifiers was evaluated using three performance indices ofsensitivity, specificity, and positive predictive value.

Sensitivity of a test is the proportion of people with the disease whohave a positive test result, the higher the sensitivity, the greater thedetection rate and the lower the false negative (FN) rate. Thespecificity of the test is the proportion of people without the diseasewho have a negative test result, the higher the specificity, the lowerthe false positive rate and the lower the proportion of people who havethe disease who will be unnecessarily worried or exposed to unnecessarytreatment. The positive predictive value (PPV) of a test is theprobability of a patient with a positive test actually having a disease.

The ROC curve is a plot of sensitivity against (1-specificity).Sensitivity, also known as true positive fraction (TFP), refers to theprobability that a test result was positive when the disease waspresent. The area under the ROC curve indicates the performance of theclassifier across the entire range of cut-off points. Conventionally,the area under the ROC curve must range between 0.5 and 1. If the areawas closer to 1, this would show that the classifier had better accuracyin the testing. Currently, the area under the ROC curve is the bestindicator for the classifier's performance with regard to themisclassification rate and the measure of risk based on confusion andloss matrices. This is because ROC was able to provide the most completeway of quantifying the diagnostic accuracy.

The ROC results based on the sensitivity, specificity, positivepredictive value and area under the curve for the three classifiers weretabulated in the following table.

Area under the Classifier TN TP FP FN Sensitivity Specificity curve +PVBPA 209 622 41 128 82.9 83.6 0.833 93.8 RBF 195 666 55 84 88.8 78 0.83492.4 Fuzzy 189 585 61 165 78 75.6 0.768 90.6 GMM 195 711 55 39 94.8 780.864 92.8 SVM 226 630 24 120 84 90.4 0.872 96.3

The results obtained from the 1000 testing data were classified undertrue negative (TN), true positive (TP), false positive (FP) and falsenegative (FN), depending on each classifier's situation. As shown in thetable, the GMM classifier showed the highest sensitivity of 94.8% amongthe five classifiers. This was followed by RBF and SVM with sensitivityof 88.8% and 84% respectively. This observation had showed that thehigher sensitivity of the classifier would result in a greater detectionrate by causing the false negative rate to be lower.

SVM showed the highest specificity of 90.4%, and this was justified bythe number of true negative cases. This result was followed by BPA withspecificity of 83.6%, and both GMM and RBF had the same specificity of78%. Fuzzy showed the least specificity of 75.6%. In tabulating thepositive predictive value, SVM classifier showed the highest value of96.3, followed by BPA with value of 93.8. The PPV values for GMM, RBFand Fuzzy were 92.8, 92.4, and 90.6 respectively.

The area under the curve is also an important parameter as it determinesthe overall classification accuracy for the five classifiers. The ROCcurves for each of the five classifiers were plotted and compared. TheSVM had the largest area under the curve, whereas the Fuzzy classifierhad the smallest area among the five classifiers. This result wasreinforced based on the area under the curve tabulated in the previoustable. It was accountable for SVM to have an area of 0.872 which is thelargest area under the curve as compared to the other four classifierswith an area of 0.768, 0.833, 0.834 and 0.864 (Fuzzy, BPA, RBF, andGMM), respectively. As seen from the results obtained, SVM was the mostaccurate classifier due to its area under the ROC curve being closer to1.

In the statistic analysis of ROC curves, SVM was considered theoutstanding classifier, even though GMM had achieved the highestsensitivity. This result was based on the four performance indices inwhich SVM had attained the best result in three of these indices. SVMhad the greatest specificity and positive predictive value and had alsoattained the largest area under the curve which implies its accuracy.Therefore, SVM was considered to be an excellent classifier.

FIG. 7 illustrates the graphical user interface in accordance with anembodiment of the present invention for the detection and classificationof breast cancer. The procedures for the interface were carried out byuploading the set of temperature data required to be classified. Thisaction was done by clicking on the push button labeled “Upload Data.”Once the data has been selected, the file name will appear on the“Input” text box. The user may then select any of the five classifierswhich they wish to test, for instance back-propagation, or GMM, etc. Thevarious stages of breast cancer are represented by the four differentimages (illustrated as an “X” in FIG. 7 for purposes of simplifying theillustration), namely: normal, benign, cancer, and suspected-cancer. Inan embodiment, a color indicator would also be used to identify thevarious stages of the breast cancer.

The classified result will be shown in the output classificationsection. For instance, if the classified result is “Cancer,” the boxnext to it will turn pink which represents “Cancer” as shown in thecolor indicator. The image which represents “Cancer” will also behighlighted in the stages of breast cancer. Under the outputclassification, the results obtained by the other algorithms are alsodisplayed. The classified result will be based on the result from themajority of the algorithms. For instance, if the result shown for mostof the algorithms is “Cancer,” then the classified result will be shownas “Cancer.” Lastly, the whole procedure can be repeated by clicking onthe push button “Reset” in order to use another classifier or to inputnew data.

The present invention reveals that the use of temperature as a tool todetect breast cancer is possible, though the performance of the currentdiscrete temperature approach will improve the addition of furthertraining data, since all of these classifiers are iterative and improvein their accuracy as more data is added, especially for much youngerfemales who are not suitable for mammograms.

The interpretive system of the present invention incorporates dynamicthermal analysis for the detection of breast cancer. In a preferredembodiment, five classifiers (neural network trained withback-propagation, radial basis function, fuzzy networks, Gaussianmixture model, and support vector machines) are used for decisionmaking. The accuracy of these classifiers depends on the size andquality of the training data, the rigor of the training imparted andalso the parameters used to represent the input, consisting of thebreast surface temperature. With more temperature data being analyzed,the five classifiers were able to achieve more than 90% of accuracy inclassifying the four different diagnoses (normal, benign, cancer, andsuspected-cancer). A significant advantage of the classifiers is thatthe system provides a detection system without human interpretation orhuman error. By using five separate methods of analyzing data from fiveindependent classifiers, it was possible to generate positive predictivevalues that provide a picture of the underlying physiology of the breastwithout requiring human interpretation of images.

While the present invention has been illustrated and described herein interms of a preferred embodiment and several alternatives, it is to beunderstood that the techniques described herein can have a multitude ofadditional uses and applications. Accordingly, the invention should notbe limited to just the particular description and various drawingfigures contained in this specification that merely illustrate apreferred embodiment and application of the principles of the invention.

1. A system for analyzing a set of temperature readings of breast tissueof a subject to identify suspect tissue and non-suspect tissue,comprising: a set of temperature sensors for sensing the set oftemperature readings over a predetermined period, each temperaturesensor among the set of temperature sensors placed at a predeterminedposition on a breast of the subject; a storage system storing the set oftemperature readings received from the set of temperature sensors; andan ensemble of classifier systems including two or more classifiersystems, the ensemble of classifier systems trained using one or moreactual temperature readings including an above average temperaturereading corresponding to suspect tissue, an average temperature readingcorresponding to non-suspect tissue, and an anomalous temperaturereading corresponding to non-suspect tissue, the ensemble of classifiersystems receiving the set of temperature readings as an input,generating two or more output labels identifying the breast tissue assuspect tissue or non-suspect tissue, and combining the two or moreoutput labels into a single diagnosis identifying the breast tissue assuspect tissue or non-suspect tissue, wherein a weight of each labelfrom the two or more output labels determines a relative contribution ofthe label towards the single diagnosis.
 2. The system as recited inclaim 1, wherein suspect tissue includes cancerous tissue, benigntissue, and suspected cancerous tissue.
 3. The system as recited inclaim 1, wherein the ensemble of classifier systems is trained using theone or more actual temperature readings corresponding to one or moreknown output labels using one or more of supervised learning,unsupervised learning, or reinforcement learning.
 4. The system asrecited in claim 1, wherein the ensemble of classifier systems includesa neural network.
 5. The system as recited in claim 4, wherein theneural network was trained using one or more actual temperature readingscorresponding to one or more known output labels using eitherback-propagation or neuroevolution.
 6. The system as recited in claim 1,wherein the ensemble of classifier systems includes two or more neuralnetworks, each neural network among the two or more neural networksprovided with different node weights, a different topology, or differentactivation functions or trained using a different set of one or moreactual temperature readings corresponding to one or more known outputlabels.
 7. The system as recited in claim 1, wherein the ensemble ofclassifier systems includes one or more of a back-propagation neuralnetwork, a neural network trained with neuroevolution, a radial basisfunction, a Gaussian mixture model, a fuzzy network, a neural network,and a support vector machine.
 8. The system as recited in claim 1,wherein the predetermined period is 24 hours or less.
 9. The system asrecited in claim 1, further comprising a portable device including amicroprocessor, the storage system contained within the portable deviceand controlled by the microprocessor, each temperature sensor connectedto the portable device by a lead among a set of leads.
 10. The system asrecited in claim 9, wherein each lead is color coded based on thepredetermined position where the temperature sensor will be placed onthe breast.
 11. The system as recited in claim 10, wherein eachtemperature sensor is labeled based on the predetermined position wherethe temperature sensor will be placed on the breast.
 12. The system asrecited in claim 9, wherein the set of temperature sensors are dividedinto a first group and a second group, each of the temperature sensorsin the first group of temperature sensors being placed in thepredetermined position on a left breast of the subject and each of thetemperature sensors in the second group of temperature sensors beingplaced in the predetermined position on a right breast of the subject.13. The system as recited in claim 1, wherein the set of temperaturesensors are divided into a first group and a second group, onetemperature sensor from the first group placed below a nipple of a leftbreast of the subject and one temperature sensor from the second groupplaced below a nipple of a right breast of the subject, one temperaturesensor from the first group placed in an upper outer quadrant of theleft breast and one temperature sensor from the second group placed inan upper outer quadrant of the right breast, one temperature sensor fromthe first group placed in an upper outer quadrant toward axillaassociated with the left breast and one temperature sensor from thesecond group placed in an upper outer quadrant toward axilla associatedwith the right breast, one temperature sensor from the first groupplaced on an upper areola of the left breast and one temperature sensorfrom the second group placed on an upper areola of the right breast, onetemperature sensor from the first group placed on a vertical midlineabove a horizontal midline of the left breast and one temperature sensorfrom the second group placed on a vertical midline above a horizontalmidline of the right breast, one temperature sensor from the first groupplaced in an upper inner quadrant of the left breast and one temperaturesensor from the second group placed in an upper inner quadrant of theright breast, one temperature sensor from the first group placed in anambient temperature zone of the left breast and one temperature sensorfrom the second group placed in an ambient temperature zone of the rightbreast, and one temperature sensor from the first group placed in aflexible position of the left breast and one temperature sensor from thesecond group placed in a flexible position of the right breast.
 14. Thesystem as recited in claim 13, wherein the flexible position of the leftbreast and the flexible position of the right breast includes in an areaof concern, near a palpable lesion, and in a contralateral position. 15.The system as recited in claim 1, further comprising a substantiallylobate shaped sensor placeholder with a set of four appendages, eachappendage among the four appendages forming a series of holes withineach appendage, each hole among the series of holes serving as alocation for placing one of the temperature sensors among the set oftemperature sensors, and wherein a central area of the sensorplaceholder forms a center hole for placement over a nipple of thebreast to align the sensor placeholder.
 16. The system as recited inclaim 15, wherein the sensor placeholder is aligned on the breast withthree of the four appendages positioned above the nipple.
 17. The systemas recited in claim 16, wherein a first sensor placeholder is alignedover a left breast and a second sensor placeholder is aligned over aright breast of the subject, wherein the set of temperature sensors aredivided into a first group and a second group, one temperature sensorfrom the first group placed within a hole of a first appendage of thefirst sensor placeholder and below a nipple of the left breast and onetemperature sensor from the second group placed within a hole of a firstappendage of the second sensor placeholder and below a nipple of theright breast, one temperature sensor from the first group placed withina hole of a second appendage of the first sensor placeholder and in anupper outer quadrant of the left breast and one temperature sensor fromthe second group placed within a hole of a second appendage of thesecond placeholder and in an upper outer quadrant of the right breast,one temperature sensor from the first group placed within a second holeof a second appendage of the first sensor placeholder and in an upperouter quadrant toward axilla associated with the left breast and onetemperature sensor from the second group placed within a second hole ofa second appendage of the second sensor placeholder and in an upperouter quadrant toward axilla associated with the right breast, onetemperature sensor from the first group placed within a hole of a thirdappendage of the first sensor placeholder and on an upper areola of theleft breast and one temperature sensor from the second group placedwithin a hole of a third appendage of the second sensor placeholder andon an upper areola of the right breast, one temperature sensor from thefirst group placed within a second hole of a third appendage of thefirst sensor placeholder and on a vertical midline above a horizontalmidline of the left breast and one temperature sensor from the secondgroup placed within a second hole of a third appendage of the secondsensor placeholder and on a vertical midline above a horizontal midlineof the right breast, one temperature sensor from the first group placedwithin a hole of a fourth appendage of the first sensor placeholder andin an upper inner quadrant of the left breast and one temperature sensorfrom the second group placed within a hole of a fourth appendage of thesecond sensor placeholder and in an upper inner quadrant of the rightbreast, one temperature sensor from the first group placed within a holeof the set of four appendages of the first sensor placeholder and in anambient temperature zone of the left breast and one temperature sensorfrom the second group placed within a hole of the set of four appendagesof the second sensor placeholder and in an ambient temperature zone ofthe right breast, and one temperature sensor from the first group placedwithin a hole of the set of four appendages of the first sensorplaceholder and in a flexible position of the left breast and onetemperature sensor from the second group placed within a hole of the setof four appendages of the second sensor placeholder and in a flexibleposition of the right breast.
 18. The system as recited in claim 1,wherein the set of temperature sensors is located on the subject,wherein the storage system and the ensemble of classifier systems islocated in a remote location, further comprising a wireless transmittertransmitting the set of temperature readings to the storage system. 19.The system as recited in claim 1, wherein the set of temperature sensorsand the storage system are located on the subject, further comprising asecond storage system located in a remote location, the second storagesystem receiving the set of temperature readings from the storagesystem, and wherein the ensemble of classifier systems receives the setof temperature readings from the second storage system.
 20. The systemas recited in claim 1, wherein the ensemble of classifier systems islocated in a remote location.
 21. The system as recited in claim 1,further comprising a computer operating the ensemble of classifiersystems, the computer including a user interface enabling a user of thecomputer to control operation of the ensemble of classifier systems andproviding the user with a visual indication of the two or more outputlabels and the single diagnosis.
 22. The system as recited in claim 21,wherein the user interface further provides a visual indication of astage of breast cancer for the breast tissue identified as suspecttissue.
 23. The system as recited in claim 21, wherein the visualindication includes a color indication.
 24. The system as recited inclaim 23, wherein the visual indication further includes a wordindication.
 25. A system for analyzing a set of temperature readings ofbreast tissue of a subject to identify suspect tissue and non-suspecttissue, comprising: a set of temperature sensors for sensing the set oftemperature readings over a predetermined period, each temperaturesensor among the set of temperature sensors placed at a predeterminedposition on a breast of the subject; and an ensemble of classifiersystems including two or more classifier systems, the ensemble ofclassifier systems trained using one or more actual temperature readingsincluding an above average temperature reading corresponding to suspecttissue, an average temperature reading corresponding to non-suspecttissue, and an anomalous temperature reading corresponding tonon-suspect tissue, the ensemble of classifier systems receiving the setof temperature readings as an input, generating two or more outputlabels identifying the breast tissue as suspect tissue or non-suspecttissue, and combining the two or more output labels into a singlediagnosis identifying the breast tissue as suspect tissue or non-suspecttissue, wherein a weight of each label from the two or more outputlabels determines a relative contribution of the label towards thesingle diagnosis.
 26. The system as recited in claim 25, wherein suspecttissue includes cancerous tissue, benign tissue, and suspected canceroustissue.
 27. The system as recited in claim 25, wherein the ensemble ofclassifier systems includes a neural network.
 28. The system as recitedin claim 25, wherein the ensemble of classifier systems includes two ormore neural networks, each neural network among the two or more neuralnetworks provided with different node weights, a different topology, ordifferent activation functions or trained using a different set of oneor more actual temperature readings corresponding to one or more knownoutput labels.
 29. The system as recited in claim 25, wherein theensemble of classifier systems includes one or more of aback-propagation neural network, a neural network trained withneuroevolution, a radial basis function, a Gaussian mixture model, afuzzy network, a neural network, and a support vector machine.
 30. Thesystem as recited in claim 25, wherein the predetermined period is 24hours or less.
 31. The system as recited in claim 25, wherein the set oftemperature sensors are divided into a first group and a second group,each of the temperature sensors in the first group of temperaturesensors being placed in the predetermined position on a left breast ofthe subject and each of the temperature sensors in the second group oftemperature sensors being placed in the predetermined position on aright breast of the subject.
 32. The system as recited in claim 25,wherein the set of temperature sensors are divided into a first groupand a second group, one temperature sensor from the first group placedbelow a nipple of a left breast of the subject and one temperaturesensor from the second group placed below a nipple of a right breast ofthe subject, one temperature sensor from the first group placed in anupper outer quadrant of the left breast and one temperature sensor fromthe second group placed in an upper outer quadrant of the right breast,one temperature sensor from the first group placed in an upper outerquadrant toward axilla associated with the left breast and onetemperature sensor from the second group placed in an upper outerquadrant toward axilla associated with the right breast, one temperaturesensor from the first group placed on an upper areola of the left breastand one temperature sensor from the second group placed on an upperareola of the right breast, one temperature sensor from the first groupplaced on a vertical midline above a horizontal midline of the leftbreast and one temperature sensor from the second group placed on avertical midline above a horizontal midline of the right breast, onetemperature sensor from the first group placed in an upper innerquadrant of the left breast and one temperature sensor from the secondgroup placed in an upper inner quadrant of the right breast, onetemperature sensor from the first group placed in an ambient temperaturezone of the left breast and one temperature sensor from the second groupplaced in an ambient temperature zone of the right breast, and onetemperature sensor from the first group placed in a flexible position ofthe left breast and one temperature sensor from the second group placedin a flexible position of the right breast.
 33. The system as recited inclaim 32, wherein the flexible position of the left breast and theflexible position of the right breast includes in an area of concern,near a palpable lesion, and in a contralateral position.
 34. The systemas recited in claim 25, further comprising a substantially lobate shapedsensor placeholder with a set of four appendages, each appendage amongthe four appendages forming a series of holes within each appendage,each hole among the series of holes serving as a location for placingone of the temperature sensors among the set of temperature sensors, andwherein a central area of the sensor placeholder forms a center hole forplacement over a nipple of the breast to align the sensor placeholder.35. The system as recited in claim 34, wherein the sensor placeholder isaligned on the breast with three of the four appendages positioned abovethe nipple.
 36. The system as recited in claim 35, wherein a firstsensor placeholder is aligned over a left breast and a second sensorplaceholder is aligned over a right breast of the subject, wherein theset of temperature sensors are divided into a first group and a secondgroup, one temperature sensor from the first group placed within a holeof a first appendage of the first sensor placeholder and below a nippleof the left breast and one temperature sensor from the second groupplaced within a hole of a first appendage of the second sensorplaceholder and below a nipple of the right breast, one temperaturesensor from the first group placed within a hole of a second appendageof the first sensor placeholder and in an upper outer quadrant of theleft breast and one temperature sensor from the second group placedwithin a hole of a second appendage of the second placeholder and in anupper outer quadrant of the right breast, one temperature sensor fromthe first group placed within a second hole of a second appendage of thefirst sensor placeholder and in an upper outer quadrant toward axillaassociated with the left breast and one temperature sensor from thesecond group placed within a second hole of a second appendage of thesecond sensor placeholder and in an upper outer quadrant toward axillaassociated with the right breast, one temperature sensor from the firstgroup placed within a hole of a third appendage of the first sensorplaceholder and on an upper areola of the left breast and onetemperature sensor from the second group placed within a hole of a thirdappendage of the second sensor placeholder and on an upper areola of theright breast, one temperature sensor from the first group placed withina second hole of a third appendage of the first sensor placeholder andon a vertical midline above a horizontal midline of the left breast andone temperature sensor from the second group placed within a second holeof a third appendage of the second sensor placeholder and on a verticalmidline above a horizontal midline of the right breast, one temperaturesensor from the first group placed within a hole of a fourth appendageof the first sensor placeholder and in an upper inner quadrant of theleft breast and one temperature sensor from the second group placedwithin a hole of a fourth appendage of the second sensor placeholder andin an upper inner quadrant of the right breast, one temperature sensorfrom the first group placed within a hole of the set of four appendagesof the first sensor placeholder and in an ambient temperature zone ofthe left breast and one temperature sensor from the second group placedwithin a hole of the set of four appendages of the second sensorplaceholder and in an ambient temperature zone of the right breast, andone temperature sensor from the first group placed within a hole of theset of four appendages of the first sensor placeholder and in a flexibleposition of the left breast and one temperature sensor from the secondgroup placed within a hole of the set of four appendages of the secondsensor placeholder and in a flexible position of the right breast. 37.The system as recited in claim 25, wherein the set of temperaturesensors is located on the subject, wherein the ensemble of classifiersystems is located in a remote location, further comprising a wirelesstransmitter transmitting the set of temperature readings to the ensembleof classifier systems.
 38. The system as recited in claim 25, whereinthe ensemble of classifier systems is located in a remote location. 39.The system as recited in claim 25, further comprising a computeroperating the ensemble of classifier systems, the computer including auser interface enabling a user of the computer to control operation ofthe ensemble of classifier systems and providing the user with a visualindication of the two or more output labels and the single diagnosis.40. The system as recited in claim 39, wherein the user interfacefurther provides a visual indication of a stage of breast cancer for thebreast tissue identified as suspect tissue.
 41. The system as recited inclaim 39, wherein the visual indication includes a color indication. 42.The system as recited in claim 41, wherein the visual indication furtherincludes a word indication.
 43. A system for analyzing a set oftemperature readings of breast tissue of a subject to identify suspecttissue and non-suspect tissue, comprising: a storage system storing theset of temperature readings, the set of temperature readings gatheredover a predetermined period from a set of temperature sensors placed ata predetermined position on a breast of the subject; and an ensemble ofclassifier systems including two or more classifier systems, theensemble of classifier systems trained using one or more actualtemperature readings including an above average temperature readingcorresponding to suspect tissue, an average temperature readingcorresponding to non-suspect tissue, and an anomalous temperaturereading corresponding to non-suspect tissue, the ensemble of classifiersystems receiving the set of temperature readings as an input,generating two or more output labels identifying the breast tissue assuspect tissue or non-suspect tissue, and combining the two or moreoutput labels into a single diagnosis identifying the breast tissue assuspect tissue or non-suspect tissue, wherein a weight of each labelfrom the two or more output labels determines a relative contribution ofthe label towards the single diagnosis.